[unreadable] This study will develop a combined computational and experimental approach for mapping transcriptional networks, with E. coli as a model organism. An experimental method based on reporter plasmids for measuring the activity of many promoters in parallel at very high temporal resolution and accuracy will be developed. This will allow collection of kinetic expression data at unprecedented amounts and accuracy from living cells. We will develop analysis algorithms that use this kinetic data to produce maps of the regulatory circuit structure. The algorithms will be based on detailed kinetic features such as correlations and delays between genes. To understand the computational function of transcriptional circuits, however, it is not enough to determine the connectivity. The effective parameters that control each reaction must also be specified. Therefore, our algorithms will determine both the connectivity of the transcriptional interactions and their relative strengths, within quantitative models of the network dynamics. This approach will be tested on selected well-defined gene systems as benchmarks. At later stages the experiment and analysis will be scaled up to the level of virtually all promoters in the organism, to map the cell-wide transcriptional network as a dynamic system. We will aim to formulate design principles that underlie the architecture of transcriptional networks. [unreadable] Furthermore, we will develop methods for interpreting and visualizing the complex wiring diagrams generated in such studies. We will develop algorithms for decomposing the complex transcriptional network into basic building blocks. This approach will generalize the notion of motifs, which is powerful in analyzing sequences and protein structures, to the level of connectivity networks. We will develop algorithms that detect recurring circuit patterns that appear more frequently than in randomized networks. The function of these 'network motifs' in information processing will be experimentally and theoretically analyzed. We expect that the analysis tools and concepts developed in E. coli will be very useful in mapping complex regulatory circuits in other cell types. [unreadable] [unreadable]